Project Summary/Abstract One of the goals of modern cancer research is to decompose the oncogenic state of individual tumors directly in terms of cellular pathways that are aberrantly activated or deregulated. Despite large-scale efforts to systematically map the cancer genome, determining how alterations within a given tumor interact to induce activated cellular states is still unknown. A promising approach is to use transcriptional signatures to determine which specific oncogenic pathways are active. However, significant challenges remain to make this approach effective. Our objective in this project is to develop a general methodology to systematically generate and validate gene expression signatures representing the activation and deregulation of 40 oncogenes and tumor suppressors and to use them to characterize human tumors. Aim 1: Generate oncogenic transcriptional signatures using experimental cell models. Aim 2: Create statistical scoring models based on the oncogenic signatures of Aim 1. Aim 3: Validate the signature models and assess their universality and tissue specificity. Aim 4: Create a publicly available database of signatures and tools to characterize tumors. This database will be broadly useful for the cancer research community and will enable a large number of clinically-relevant applications such as functional annotation of cancer genomes, pathway-based classification of individual cell line and tumors, improving clinical prognosis in multiple disease subtypes, better risk stratification of patients, and predicting response for targeted therapeutic interventions. For over a decade we have been identifying transcriptional signatures for a variety of problems including molecular sub-typing, prediction of chemo-sensitivity and clinical outcome in many tumor types and making them available to the research community. Based on our previous experience, preliminary results and a detailed understanding of the experimental, statistical, software and database generation challenges, and the fact that we are bringing together a broad interdisciplinary group of researchers with strength in primary cellular transformation, data and statistical analysis, along with collaborators who can contribute additional, important genomic data and resources, we believe we are well poised to move forward with this project.